CCF-BSF:CIF: Small: Coding for Fast Storage Access and In-Memory Computing

CCF-BSF:CIF：小型：快速存储访问和内存计算的编码

• 批准号: 1718389
• 负责人: Lara Dolecek
• 金额: $ 47万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718389&HistoricalAwards=false
• 关键词: CCF; BSF; CIF; Small; Coding

Part 1:Driven by the needs of mobile and cloud computing, demand for data storage is exhibiting steep growth, both in the direction of higher storage density as well as a simultaneous ambitious increase in access performance. A related exciting emerging trend driven by access challenges is in-memory computing, whereby computations are offloaded from the main processing units to the memory to reduce transfer time and energy. The challenges of future rapid storage access and in-memory computing cannot be addressed by the conventional storage architectures that inevitably trade off reliability and capacity for latency. This bottleneck calls for innovative research contributions that can simultaneously maximize the storage density, access performance, and computing functionality. This project addresses this imminent challenge by developing principled mathematical foundations that will underpin future computing systems possessing qualities necessary to address new data-intensive applications, focusing on fundamental performance bounds, algorithms, and practical channel coding methods. The results of this project will be demonstrated on modern data-driven and machine learning applications, will advance the repertoire of mathematical techniques in information sciences, and will directly impact future computer system architectures to meet the growing and wide ranging societal and scientific needs for computing and rapid data processing. Additionally, the proposal offers several mechanisms for broader impacts, including engagement with data storage and memory industry through the existing research center that the principal investigator is leading at UCLA, curriculum development and the introduction of new graduate courses in the UCLA on-line master's program in engineering, engagement of undergraduate researchers, and dissemination of the results through survey-style articles and tutorials.Part 2: The project has the following three complementary research goals:1) Invention of new channel codes for reliable and fast memory access for latency sensitive applications, with the study spanning general memories and specific schemes for resistive memories in particular. The proposed schemes will offer non-trivial extensions to vibrant coding subjects: codes with locality (algebraic and graph-based) and constrained coding; 2) Invention of new channel codes for which the decoding is performed directly in memory to enable simultaneously satisfying competing requirements on latency and reliability. Here, the decoder itself is subject to computational errors, themselves manifested in a data dependent sense. The analysis will lead to bounds and practical code designs robust to data-dependent errors. An exemplar will be codes designed using spatial coupling and decoded using windowed message passing decoders;3) Development of novel fundamental bounds, algorithms, and channel codes for robust in-memory computing, with the focus on quantifying the robustness of computing primitives in statistical inference and other machine learning algorithms used in modern data-driven applications. These include fundamental performance limits and new coding-based methods to simultaneously combat sneak paths and computing noise. Analysis will include coding for (noisy) Hamming/Euclidean similarity calculations, evaluated in the context of practical machine learning applications.Results from this project will also contribute to the curriculum development at UCLA and will offer new opportunities for the engagement of undergraduate researchers from underrepresented groups.

第 1 部分：在移动和云计算需求的推动下，数据存储的需求呈现出急剧增长的趋势，既朝着更高存储密度的方向发展，又同时在访问性能方面大幅提高。由访问挑战驱动的一个相关的令人兴奋的新兴趋势是内存计算，其中计算从主处理单元卸载到内存以减少传输时间和能量。传统存储架构无法解决未来快速存储访问和内存计算的挑战，传统存储架构不可避免地要牺牲可靠性和容量以换取延迟。这一瓶颈需要创新的研究贡献，以同时最大化存储密度、访问性能和计算功能。该项目通过开发原则性的数学基础来解决这一迫在眉睫的挑战，这些基础将支撑未来的计算系统，这些系统具有解决新数据密集型应用程序所需的品质，重点关注基本性能范围、算法和实用的信道编码方法。该项目的成果将在现代数据驱动和机器学习应用程序中得到证明，将推进信息科学中数学技术的发展，并将直接影响未来的计算机系统架构，以满足日益增长和广泛的社会和科学计算需求和快速的数据处理。此外，该提案还提供了多种产生更广泛影响的机制，包括通过首席研究员在加州大学洛杉矶分校领导的现有研究中心参与数据存储和内存行业、课程开发以及在加州大学洛杉矶分校在线硕士课程中引入新的研究生课程工程方面，本科生研究人员的参与，以及通过调查式文章和教程传播结果。第 2 部分：该项目有以下三个互补的研究目标：1）发明新的通道代码，用于对延迟敏感的可靠且快速的内存访问应用程序，研究范围涵盖一般记忆特别是电阻式存储器的具体方案。所提出的方案将为充满活力的编码主题提供重要的扩展：具有局部性（代数和基于图）和约束编码的代码； 2)发明新的信道代码，其解码直接在存储器中执行，以能够同时满足延迟和可靠性方面的竞争要求。在这里，解码器本身会受到计算错误的影响，这些错误本身就表现在数据相关的意义上。该分析将得出对数据相关错误具有鲁棒性的边界和实际代码设计。一个范例是使用空间耦合设计代码并使用窗口消息传递解码器进行解码；3) 开发用于鲁棒内存计算的新颖基本边界、算法和通道代码，重点是量化统计推理中计算原语的鲁棒性以及现代数据驱动应用程序中使用的其他机器学习算法。 其中包括基本性能限制和新的基于编码的方法，以同时对抗潜行路径和计算噪声。分析将包括（嘈杂的）汉明/欧几里得相似性计算的编码，并在实际机器学习应用的背景下进行评估。该项目的结果还将有助于加州大学洛杉矶分校的课程开发，并将为来自代表性不足的本科研究人员的参与提供新的机会组。

项目成果

A Channel-Aware Combinatorial Approach to Design High Performance Spatially-Coupled Codes

设计高性能空间耦合代码的通道感知组合方法

• DOI: 10.1109/tit.2020.2979981
• 发表时间: 2018-04-16
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Ahmed Hareedy;R. Wu;L. Dolecek
• 通讯作者: L. Dolecek

Hamming Distance Computation in Unreliable Resistive Memory

不可靠电阻存储器中的汉明距离计算

• DOI: 10.1109/tcomm.2018.2840717
• 发表时间: 2018-11
• 期刊: IEEE Transactions on Communications
• 影响因子: 8.3
• 作者: Chen, Zehui;Schoeny, Clayton;Dolecek, Lara
• 通讯作者: Dolecek, Lara

Channel Coding for Nonvolatile Memory Technologies: Theoretical Advances and Practical Considerations

非易失性存储器技术的通道编码：理论进展和实际考虑

• DOI: 10.1109/jproc.2017.2694613
• 发表时间: 2017-05-05
• 期刊: Proceedings of the IEEE
• 影响因子: 20.6
• 作者: L. Dolecek;Yuval Cassuto
• 通讯作者: Yuval Cassuto

Coding Assisted Adaptive Thresholding for Sneak-Path Mitigation in Resistive Memories

用于减少电阻存储器中潜行路径的编码辅助自适应阈值

• 发表时间: 2019-01
• 期刊: IEEE Information Theory Workshop
• 作者: Chen, Zehui;Schoeny, Clayton;Dolecek, Lara
• 通讯作者: Dolecek, Lara

Error Correction and Detection for Computing Memories Using System Side Information

使用系统侧信息对计算存储器进行纠错和检测

• DOI: 10.1109/itw.2018.8613473
• 发表时间: 2018-11
• 期刊: IEEE Information Theory Workshop
• 作者: Schoeny, Clayton;Alam, Irina;Gottscho, Mark;Gupta, Puneet;Dolecek, Lara
• 通讯作者: Dolecek, Lara